# Batch face detection on a folder of images
# Usage: python batch_process.py --input images/ --output results/

import argparse
from pathlib import Path

import cv2
from tqdm import tqdm

from uniface import SCRFD, RetinaFace
from uniface.visualization import draw_detections


def get_image_files(input_dir: Path, extensions: tuple) -> list:
    files = []
    for ext in extensions:
        files.extend(input_dir.glob(f"*.{ext}"))
        files.extend(input_dir.glob(f"*.{ext.upper()}"))
    return sorted(files)


def process_image(detector, image_path: Path, output_path: Path, threshold: float) -> int:
    """Process single image. Returns face count or -1 on error."""
    image = cv2.imread(str(image_path))
    if image is None:
        return -1

    faces = detector.detect(image)

    # unpack face data for visualization
    bboxes = [f["bbox"] for f in faces]
    scores = [f["confidence"] for f in faces]
    landmarks = [f["landmarks"] for f in faces]
    draw_detections(image, bboxes, scores, landmarks, vis_threshold=threshold)

    cv2.putText(
        image,
        f"Faces: {len(faces)}",
        (10, 30),
        cv2.FONT_HERSHEY_SIMPLEX,
        1,
        (0, 255, 0),
        2,
    )
    cv2.imwrite(str(output_path), image)

    return len(faces)


def main():
    parser = argparse.ArgumentParser(description="Batch process images with face detection")
    parser.add_argument("--input", type=str, required=True, help="Input directory")
    parser.add_argument("--output", type=str, required=True, help="Output directory")
    parser.add_argument("--detector", type=str, default="retinaface", choices=["retinaface", "scrfd"])
    parser.add_argument("--threshold", type=float, default=0.6, help="Visualization threshold")
    parser.add_argument("--extensions", type=str, default="jpg,jpeg,png,bmp", help="Image extensions")
    args = parser.parse_args()

    input_path = Path(args.input)
    output_path = Path(args.output)

    if not input_path.exists():
        print(f"Error: Input directory '{args.input}' does not exist")
        return

    output_path.mkdir(parents=True, exist_ok=True)

    extensions = tuple(ext.strip() for ext in args.extensions.split(","))
    image_files = get_image_files(input_path, extensions)

    if not image_files:
        print(f"No images found with extensions {extensions}")
        return

    print(f"Found {len(image_files)} images")

    detector = RetinaFace() if args.detector == "retinaface" else SCRFD()

    success, errors, total_faces = 0, 0, 0

    for img_path in tqdm(image_files, desc="Processing", unit="img"):
        out_path = output_path / f"{img_path.stem}_detected{img_path.suffix}"
        result = process_image(detector, img_path, out_path, args.threshold)

        if result >= 0:
            success += 1
            total_faces += result
        else:
            errors += 1
            print(f"\nFailed: {img_path.name}")

    print(f"\nDone! {success} processed, {errors} errors, {total_faces} faces total")


if __name__ == "__main__":
    main()